Metrics & KPIs for Product Management

Why Metrics Matter

Metrics are how we know if we’re succeeding. Without measurement, product decisions are opinions. With measurement, they’re informed choices.

“What gets measured gets managed.” — Peter Drucker

Types of Product Metrics

Input Metrics vs. Output Metrics

Type	Definition	Example
Input (Leading)	Activities we control	Features shipped, experiments run
Output (Lagging)	Results we want	Revenue, retention, satisfaction

Focus on outcomes (output) but track inputs to understand causation.

The Metrics Hierarchy

                    ┌─────────────────┐
                    │  North Star     │  ← Company-level
                    │    Metric       │
                    └────────┬────────┘
                             │
              ┌──────────────┼──────────────┐
              ▼              ▼              ▼
        ┌──────────┐  ┌──────────┐  ┌──────────┐
        │ Product  │  │ Product  │  │ Product  │  ← Product-level
        │ KPIs     │  │ KPIs     │  │ KPIs     │
        └────┬─────┘  └────┬─────┘  └────┬─────┘
             │             │             │
        ┌────┴────┐   ┌────┴────┐   ┌────┴────┐
        │ Feature │   │ Feature │   │ Feature │  ← Feature-level
        │ Metrics │   │ Metrics │   │ Metrics │
        └─────────┘   └─────────┘   └─────────┘

The AARRR Framework (Pirate Metrics)

A classic framework for measuring the customer journey:

Acquisition

Question: How do users find us?

Metric	Definition
Traffic sources	Where visitors come from
Cost per acquisition (CPA)	Marketing spend / new users
Conversion to signup	Visitors → Signups

Activation

Question: Do users have a good first experience?

Metric	Definition
Time to value	How long until first success
Onboarding completion	% completing key steps
Activation rate	% reaching “aha moment”

Retention

Question: Do users come back?

Metric	Definition
Retention rate	% still active after N days/months
Churn rate	% leaving per period
DAU/MAU ratio	Daily/monthly active user engagement

Revenue

Question: Are we making money?

Metric	Definition
MRR/ARR	Monthly/Annual recurring revenue
ARPU	Average revenue per user
LTV	Lifetime value of a customer
Expansion revenue	Revenue from existing customers

Referral

Question: Do users tell others?

Metric	Definition
NPS	Net Promoter Score
Referral rate	% of users who refer others
Viral coefficient	Referrals per user

B2B SaaS Metrics

Revenue Metrics

Metric	Formula	Why It Matters
ARR	Sum of annual contract values	Overall business health
MRR	Sum of monthly contract values	Monthly health
Net Revenue Retention (NRR)	(Start MRR + Expansion - Churn - Contraction) / Start MRR	Growth from existing customers
Gross Revenue Retention (GRR)	(Start MRR - Churn - Contraction) / Start MRR	Customer retention health

Customer Metrics

Metric	Formula	Target
Customer Acquisition Cost (CAC)	Sales & Marketing spend / New customers	Lower is better
LTV:CAC Ratio	Customer LTV / CAC	>3:1 is healthy
Payback Period	CAC / Monthly revenue	<12 months ideal
Logo Retention	Customers retained / Total customers	>90% for B2B

Engagement Metrics

Metric	Definition
DAU/WAU/MAU	Active users by period
Feature adoption	% of users using specific features
Session frequency	How often users return
Time in product	Engagement depth

Product-Specific Metrics

For Data Products (Path2Response Context)

Metric	Definition	Why It Matters
Match rate	Records matched / Records submitted	Data quality indicator
Data freshness	Age of most recent update	Competitive differentiator
Audience performance	Response rates, ROI	Customer success
Query volume	API calls, exports	Usage and engagement
Coverage	% of addressable market in database	Scale indicator

For Platform Products

Metric	Definition
API uptime	Availability percentage
API latency	Response time (p50, p95, p99)
Error rate	Failed requests / Total requests
Developer adoption	# of integrations, active developers

Setting Good Metrics

SMART Criteria

Letter	Meaning	Example
S	Specific	“Increase retention” → “Increase 90-day retention”
M	Measurable	Can track with data
A	Achievable	Realistic given resources
R	Relevant	Connected to business goals
T	Time-bound	“by end of Q2”

Good Metrics vs. Vanity Metrics

Good Metric	Vanity Metric
Activation rate	Total signups
Monthly active users	Registered users
Retention rate	Total accounts
Revenue per customer	Press mentions

Test: Does this metric drive decisions? Can we act on it?

Metric Anti-Patterns

Anti-Pattern	Problem	Solution
Too many metrics	Analysis paralysis	Focus on 3-5 key metrics
Vanity metrics	Feels good, no insight	Tie to business outcomes
Gaming	Optimizing metric, not goal	Use balanced scorecard
Short-term focus	Sacrifice long-term health	Include leading indicators
No baseline	Can’t measure improvement	Establish baselines first

Path2Response Metrics Framework

North Star Metric

Revenue from recurring customers (ARR × retention)

Product KPIs

Area	Key Metrics
Acquisition	New customers, pipeline generated
Activation	First campaign delivered, time to first order
Retention	97% client retention (company goal)
Revenue	ARR, expansion revenue, ARPU
Data Quality	Match rates, data freshness, coverage

Feature-Level Metrics Examples

Feature	Success Metrics
Digital Audiences	Segment adoption, campaign performance lift
Path2Contact	Append match rate, client satisfaction
Path2Ignite	Campaigns run, response rate improvement
Path2Linkage	Partner integrations, query volume

Operational Metrics

Metric	Target
Data update frequency	Weekly (transactions), Daily (digital)
API uptime	99.9%
Support response time	<4 hours
Onboarding time	<2 weeks

Building a Metrics Dashboard

Dashboard Principles

Hierarchy: Start with top-level, drill down
Context: Include trends, comparisons, targets
Actionability: Show metrics someone can act on
Freshness: Update frequency matches decision cadence

Dashboard Layout

┌─────────────────────────────────────────────────────┐
│  NORTH STAR: [Metric] [Trend] [vs Target]           │
├─────────────────────┬───────────────────────────────┤
│  Revenue            │  Retention                    │
│  • ARR: $X          │  • Logo retention: X%         │
│  • Growth: +X%      │  • NRR: X%                    │
│  • Pipeline: $X     │  • Churn: X customers         │
├─────────────────────┼───────────────────────────────┤
│  Acquisition        │  Engagement                   │
│  • New customers: X │  • Active customers: X        │
│  • Win rate: X%     │  • Feature adoption: X%       │
│  • CAC: $X          │  • Campaigns run: X           │
├─────────────────────┴───────────────────────────────┤
│  Data Quality                                       │
│  • Match rate: X%  • Freshness: X days  • Coverage  │
└─────────────────────────────────────────────────────┘

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